从使用混合采样方法和基于树的分类器的不平衡结直肠癌数据集的生存预测
Sadegh Soleimani1, Mahsa Bahrami1, Mansour Vali2
1Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, 16315-1355, 1631714191, Tehran, Iran.
Scientific reports
|April 25, 2025
概括
这项研究开发了算法来预测结直肠癌存活率,特别是对于1年存活率预测任务,这是高度不平衡的. 提出的方法显著改善了少数患者的死亡率预测.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 大肠直肠癌 (CRC) 的死亡率很高 (64.5%).
- 预测CRC患者的存活率对于治疗决策至关重要.
- 用于CRC生存预测的临床数据分析面临挑战,特别是不平衡的结果.
研究的目的:
- 开发用于预测结直肠癌患者1年,3年和5年的生存期的算法.
- 为了应对在生存预测中不平衡数据集的挑战.
- 改善对CRC患者少数群体的死亡率预测.
主要方法:
- 利用来自SEER数据库的结直肠癌数据集,以不平衡的生存结果.
- 应用数据预处理,包括处理缺失值和合并低频类别.
- 采用了数据平衡技术:编辑近邻 (ENN),重复编辑近邻 (RENN),合成少数群体过量采样技术 (SMOTE) 和SMOTE-RENN管道.
- 使用基于树的分类器:决策树,随机森林,额外树,梯度增强和轻梯度增强机 (LGBM).
- 使用5倍交叉验证评估的性能.
主要成果:
- 对于1年生存预测,使用LGBM的拟议方法实现了72.30%的灵敏度.
- 对于3年生存期,RENN与LGBM相结合产生了80.81%的灵敏度,在不平衡的数据上证明了有效性.
- 对于5年生存预测,LGBM获得了63.03%的灵敏度.
- 以LGBM为预测因素的RENN跟随SMOTE方法,显示出对1年和3年生存率的优越灵敏度.
- 对于5年生存预测任务,LGBM在F1分数中表现优于其他模型.
结论:
- 开发的算法有效地预测结直肠癌的生存率,特别是在高度不平衡的场景中.
- 提出的方法显著提高了代表性不足的患者群体的死亡率预测准确度.
- 数据平衡技术 (RENN,SMOTE) 和高级分类器 (LGBM) 的组合为CRC存活分析提供了强大的方法.
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